The world’s captivation with artificial intelligence (AI) and its economic potential is undeniable. Recent studies suggest that advances in generative AI alone could inject trillions of US dollars into the global economy.
However, the enthusiasm for this nascent technology unfolds during an unprecedented climate crisis that is prompting industries to accelerate their sustainability efforts.
This leads us to two crucial questions: Beyond economic gains, how can AI contribute to sustainability transitions? Where can AI’s commercial and environmental potential converge to swiftly develop scalable solutions, turning a climate-positive future from a distant aspiration into a near-term reality?
Environmental footprint of aviation industry
The aviation industry faces intense scrutiny regarding its sustainability due to the weight of its environmental footprint.
Aviation alone contributes approximately 2% of global energy-related carbon dioxide (CO2) emissions, yet organizations like the International Air Transport Association have set ambitious net-zero targets for 2050.
Additionally, an estimated 50% of the required carbon emission reductions to reach net-zero will come from yet-to-be-invented solutions.
AI is uniquely positioned to expedite these discoveries and, in turn, accelerate the industry’s sustainability transition. Its unparalleled ability to enhance our understanding of complex problems, optimize existing systems and expedite ground-breaking scientific advancements places AI at the forefront of driving sustainability progress.
With that in mind, below we aim to illustrate how AI can help us address sustainability challenges unique to the aviation industry and which organizations are leading the way.
How AI can make aviation fuel more sustainable
At the heart of the aviation industry’s decarbonization efforts lies the promise of sustainable aviation fuel (SAF), offering a tangible path to significantly reduce emissions from air travel.
Derived from renewable resources, organic materials, or waste feedstocks, SAF presents a compelling alternative to conventional jet fuel.
However, while the potential of SAF to mitigate emissions is undeniable, its accessibility and cost-competitiveness on commercial scales remain significant barriers.
AI can address these obstacles and accelerate SAF development by optimizing production processes. For instance, by analysing vast datasets, AI can pinpoint areas for efficiency improvement, leading to reduced production costs and improved economic feasibility.
Additionally, AI-driven predictive models help in identifying the most suitable feedstocks and refining conversion processes.
By considering factors such as climate data, crop yields and waste availability, AI can uncover innovative sustainable aviation fuel pathways that are both sustainable and economically viable.
A prime illustration of this potential can be seen in LanzaJet, a pioneering company at the forefront of sustainable aviation fuel (SAF) development.
By harnessing the power of AI and data analytics, LanzaJet identifies promising feedstock sources, optimizes production processes, and fine-tunes SAF blends for enhanced performance and reduced emissions.
This innovative approach accelerates the creation of environmentally friendly aviation fuels, helping to curb the carbon footprint of the aviation industry, and serves as an example of how AI can revolutionize sustainable aviation fuel development by making SAF more accessible and affordable.
AI could predict and prevent contrail formation
Contrails, those thin white lines trailing airplanes, have a surprisingly large climate impact. The 2022 IPCC report noted that clouds created by contrails account for roughly 35% of aviation’s global warming impact – which is over half the impact of the world’s jet fuel.
Unlike typical greenhouse gas emissions, contrail formation depends on specific atmospheric conditions, namely humidity and temperature. Notably, not every flight kilometre yields the same contrail impact, with a minority of flights responsible for most contrail-induced warming.
This contrasts with other emissions which scale per flight kilometre. The contrails challenge is understanding which flights will form them – and this is where AI can push the boundaries of what’s possible.
AI can compensate by synthesising signals from multiple data sources. By combining weather, satellite and flight data, AI can predict when and where contrails are likely to form – which can then be used to adjust the altitudes of flights to avoid contrail formation.
AI can also be used to confirm whether attempted contrail avoidance was successful. We leverage satellite imagery and computer vision to detect contrails and attribute them to associated flights, based on hundreds of hours of labelling tens of thousands of satellite images.
To this end, Google collaborated with American Airlines and Breakthrough Energy to leverage these AI-based tools to prove that contrail avoidance is possible in normal operations.
The experiment was deemed successful as it led to meaningful cross-organizational learnings and demonstrated a 54% reduction in contrail formation.
This collaboration also illustrates that AI is uniquely positioned to accelerate contrail avoidance as a scalable sustainability solution that can mitigate 35% of aviation’s climate impact.
AI for aircraft design and optimization
Another promising application to note is AI’s potential to help design more fuel-efficient aircrafts by analysing massive datasets and running simulations to optimize aerodynamics, materials and engine performance.
Global aviation giant Boeing exemplifies this trend through its advanced technology programmes. Boeing utilizes AI and machine learning to improve aerodynamics, materials and engine performance.
Meanwhile, computational fluid dynamics simulations are deployed to model airflow over the aircraft’s surfaces, resulting in the optimization of wing shapes and other crucial components, ultimately minimizing drag and enhancing fuel efficiency.
Generative design algorithms are also harnessed to create innovative and lightweight aircraft structures – an important step in reducing fuel consumption and emissions.
Another illustrative example of AI’s potential in aircraft design can be found in Boom Supersonic’s endeavours. The company employs generative design algorithms and advanced computer simulations to optimize the design of their Overture aircraft.
These sophisticated algorithms explore a multitude of design possibilities, generating an aircraft structure that is both lightweight and aerodynamically efficient and strong.
Such AI-driven innovations can play a critical role in enhancing fuel efficiency and reducing the ecological footprint of the aviation industry, thereby working towards a greener and more sustainable future for air travel.
AI can accelerate aviation’s net-zero transition
These examples above are but a few that illustrate the fundamental role AI has to play in accelerating the aviation industry’s net-zero transition. Given the critical timeline we face considering climate change, the development and deployment of novel AI solutions needs to be accelerated.
Critical barriers, such as the gap between technical expertise and climate domain expertise in developing AI solutions can be bridged by multi-stakeholder collaborations, as illustrated by the pioneering efforts of Google in this field and its recent efforts in partnering with European organizations to expand the range of their AI solutions to European airspace.
These efforts are exemplary of the international appetite to collaboratively work towards developing and scaling green technologies for the aviation’s sustainability transition. But further AI innovations are needed to accelerate the industry’s move towards net zero.
In line with such efforts, the World Economic Forum, together with key partners, launched the UpLink Sustainable Aviation Challenge, aimed at accelerating novel technology pathways by fostering closer collaboration between prominent start-ups and established organizations. To find out more, visit Uplink.